Advances in plasma and cleaning processing have provided for growth in the semiconductor industry. During plasma processing, hundreds of substrates may be processed. However, not all processed substrates are of acceptable quality standard. To identify the substrates that may not be of acceptable quality standard, fault detection may be implemented. As discussed herein, fault detection refers to the process of identifying potential problematic substrates.
Unacceptable processed substrates may be identified through several different fault detection methods. One method is to manually measure each substrate. However, the process of measuring hundreds of substrates in a production environment may not only be time consuming and costly, but may also be prone to human error.
Alternatively, fault detection may be accomplished by comparing data collected for each substrate against a base line data. As the term is employed herein, baseline data refers to reference measurement that is established to determine the acceptability of a processed substrate. To determine fault detection for each substrate, a plethora of baselines may be established. The number of baselines being established may depend upon the type of data (e.g., voltage bias) being collected and/or monitored.
Generally, an individual (e.g., process engineer) may manually determine the baselines based on his expertise by analyzing the data collected from one or more substrates. To facilitate discussion, FIG. 1 shows a simple diagram of a plasma processing chamber environment, in which signal data may be gathered. Consider the situation wherein, for example, a batch of substrates 102 will be processed. A first substrate 104 is placed into a plasma processing chamber 106. Signal data 108 is collected for substrate 104. Substrate 104 may be measured to determine the suitably of the substrate. Once substrate 104 has been measured, the next substrate in batch of substrates 102 is measured. A large sample of substrates may have to be manually measured in order to create an accurate baseline.
In establishing a baseline, the individual may gather the signal data (e.g., substrate bias voltage measurement) collected for the substrates that are considered acceptable. Then the individual may analyze the signal data to determine the baseline based on his expertise. In an example, if substrate 104 is considered acceptable, the process engineer may include the signal data collected for substrate 104 in establishing baselines for determining the acceptable quality of a substrate in a particular plasma processing chamber, such as plasma processing chamber 106.
In addition, for each baseline, soft and hard tolerance level ranges may be established to determine when a substrate may be considered as unacceptable. As discussed herein, a soft tolerance level and hard tolerance level refer to a percentage difference above and/or below a baseline. The hard tolerance level range also encompasses the soft tolerance range. The substrate may usually be considered acceptable as long as the substrate falls within the hard tolerance level range. Depending upon the client's requirement, the substrates whose signal data fall outside the soft tolerance range may warrant attention and an alarm may be issued.
The process of establishing each baseline, soft tolerance levels, and hard tolerance levels may be a subjective manual process. In other words, the accuracy of the baseline, soft tolerance levels, and hard tolerance levels may be dependent upon the knowledge and skill of the individual(s). In addition, the baseline, soft tolerance levels, and hard tolerance levels may shift over time due to normal operations.
In an example after processing an x number of substrates, certain plasma processing chamber hardware (e.g., o-rings) may have been worn out. In another example, continuous processing may have resulted in deposition accumulating inside the plasma processing chamber. As the conditions of the chamber and hardware change, baselines may shift. The baselines need to account for the changes in the condition of the chamber and hardware to accurately perform fault detection. If not, the acceptability of a substrate may be based on baselines that may no longer be accurate. Thus inaccurate baselines may result in costly errors, such as faulty substrates being retained and/or acceptable substrates being discarded.